Adaptive algorithm for cloud admission policies

ABSTRACT

Disclosed is a novel system and method for managing requests for an additional virtual machine. The method begins with operating at least one virtual machine accessing at least one computer resource associated with at least one physical machine within a computing cluster. One or more non-deterministic virtual machine requests for the computer resource are received. An over-utilization of the computer resource as a probability distribution function is modeled. In one example, the probability distribution function is a Beta distribution function to represent a one of a plurality of probability distribution functions. Next, an additional virtual machine on the physical machine associated with the computer resource is added in response to a probability of a utilization of the computer resource being greater than a probalistic bound on the over-utilization of the computer resource. Otherwise, the additional virtual machine is not added.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims priority from prior U.S.patent application Ser. No. 15/086,755, filed on Mar. 31, 2016, now[allowed], U.S. patent application Ser. No. 13/966,792, filed on Aug.14, 2013, now U.S. Pat. No. 9,391,919, the entire disclosure of which isherein incorporated by reference in its entirety.

BACKGROUND

The present invention generally relates to cloud computing, and morespecifically to managing the admission of virtual machines in a cloudenvironment with dynamic resource demands.

In a Cloud or computing cluster system managing the utilization ofcomputer resources with effective admission control policies isessential. Admission control policies ensure that sufficient resourcesare available in a cluster to provide fail-over protection and to ensurethat virtual machine resource reservations are respected. If theadditional computer resources are not reserved, the power-on attemptfails and the fail-over protections cannot be realized. Hence, admissioncontrol policies reserve resources to ensure robustness for a potentialfail-over and successful power-on procedures.

Each resource in a physical machine in the Cloud system has anover-utilization threshold. When this threshold is exceeded and theresources are over-utilized for longer periods of time, operations ofphysical machines may be interrupted or migration may become necessary.Over-utilization threshold is the maximum acceptable utilizationpercentage for a resource. As an example, if the over-utilizationthreshold is 90%, it is assumed that the operations will not beinterrupted, as long as the resource utilization remains below 90%. Inaddition to over-utilization threshold, the likelihood of the resourcebeing over-utilized is another parameter to be considered for thepurpose of admission control. The second threshold is the percentage oftime that the over-utilization can be tolerated. In other words, thelikelihood of finding the resource over-utilized. Note that theover-utilization threshold can be exceeded for short periods of time. Ifthe likelihood of fail-over or the virtual machine power-on isnegligible, exceeding the over-utilization threshold may be tolerated.

Admission control policies adopt admission criteria by which admissioncontrol schema accepts or rejects a request to be placed in the Cloud.In general, admission control schemas are either parameter-based ormeasurement-based. Parameter-based admission control schemas are basedon apriori knowledge of the input requests and provide for deterministicguarantees for uninterrupted Cloud operations. Examples of this type ofadmission control schemas include admitting a VM request based on itsresource demand characteristics, such as maximum resource demand oraverage resource demand Parameter-based schemas are easy to implementand guarantee Cloud operations under worst-case assumptions. Ameasurement-based admission control schema, on the other hand, utilizesthe estimated resource utilization of physical machines in addition toinput VM request parameters. In this case, the utilization of a resourceis characterized by its stochastic properties and a probabilistic boundcan be defined for the potential interruptions of Cloud operations. Theprobability density function (pdf) of the utilization of a resource isthe convolution of all the resource demands of the accepted requeststhat utilize that resource. In such aggregation of independent resourcedemands, the probability that the aggregate utilization will reach thesum of the peak demand is infinitesimally small. Using the pdf of theaggregated resource utilization in admission criteria provides forprobabilistic guarantees. That is, instead of providing deterministicbound for the worst case scenarios, measurement-based admission controlpolicies guarantee a bound on the probability of over-utilization. Inmathematical terms, resource k is stable if its utilization, U_(k),satisfies the following constraint,

P(U _(k) >U _(k) ^(o))≤ε^(o)  (1)

where U^(o) is the over-utilization threshold and ε^(o) is theprobabilistic bound on over-utilization.

BRIEF SUMMARY

We consider the problem of admitting sets of, possibly heterogeneous,virtual machines (VMs) with stochastic resource demands onto physicalmachines (PMs) in a Cloud environment. The objective is to achieve aspecified quality-of-service related to the probability of resourceover-utilization in an uncertain loading condition, while minimizing therejection probability of VM requests. We introduce a method which relieson approximating the probability distribution of the total resourcedemand on PMs and estimating the probability of over-utilization. Wecompare our method to two simple admission policies: admission based onmaximum demand and admission based on average demand We investigate theefficiency of the results of using our method on a simulated Cloudenvironment where we analyze the effects of various parameters(commitment factor, coefficient of variation etc.) on the solution forhighly variate demands.

Disclosed is an automated system and method for managing requests for anadditional virtual machine. The method begins with operating at leastone virtual machine accessing at least one computer resource associatedwith at least one physical machine within a computing cluster. One ormore non-deterministic virtual machine requests for the computerresource are received. An over-utilization of the computer resource as aprobability distribution function is modeled. In one example, theprobability distribution function is a Beta distribution function torepresent a one of a plurality of probability distribution functions.Next, an additional virtual machine on the physical machine associatedwith the computer resource is added in response to a probability of autilization of the computer resource being less than a probalistic boundon the over-utilization of the computer resource. Otherwise, theadditional virtual machine is not added. The probability distributionfunction is automatically updated in response to an additionalnon-deterministic virtual machine request, a change in the utilizationof the computer resource by the virtual machine, a change in theutilization of the computer resource by any additional virtual machinewhich has been added, or a combination thereof. Likewise, based on theprobability distribution function being updated, reevaluating theadmission of the additional virtual machine on the physical machineassociated with the computer.

In one example, the probability of the utilization of the computerresource is less than an over-utilization threshold U_(k)* is given byF_(z) _(k) _(n) (U_(k)*)≥ε_(k) where the computer resource k associatedwith the physical machine, the probabilistic bound is ε_(k), and F₄ is afunction of a sum of n independent k^(th) resource demands. Theprobalistic bound ε_(k), and/or the over-utilization threshold is U_(k)*is set, typically by a Cloud system administrator or user, based on anoperating specification of the physical machine.

In another example, a maximum number of allowed concurrent computerresource requests in a policy for the physical machine is given by aminimum of a supreme subset of independent computational resourcerequests and the probability of the utilization of the computerresource. The maximum number of allowed concurrent computer resourcerequests N_(max) in a policy is given by

${N_{\max} = {\min\limits_{k \in K}\left\{ {\sup \left\{ {n{{F_{Z_{k}^{n}}\left( U_{k}^{*} \right)} \geq \left( {1 - ɛ_{k}} \right)}} \right\}} \right\}}},$

where sup is a supremum subset and n is a sum of independent k^(th)computer resource request.

In yet another example, an over commitment placement policy with amaximum value of demand is given by

$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\left. {\kappa \mspace{11mu} C_{k}}\mspace{11mu} \right\rfloor}{D_{k}^{\max}} \right\rfloor \right\}}$

and a value of κ is solved by using N_(max), where, D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.

In still another example, an under commitment policy with an averagevalue of demand is given by

$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\theta \; C_{k}}{\mu_{D_{k}}\;} \right\rfloor \right\}}$

and a value of θ is solved by using N_(max), where D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures wherein reference numerals refer to identicalor functionally similar elements throughout the separate views, andwhich together with the detailed description below are incorporated inand form part of the specification, serve to further illustrate variousembodiments and to explain various principles and advantages all inaccordance with the present invention, in which:

FIG. 1 is a system architecture;

FIG. 2(a), FIG. 2(b), and FIG. 2(c) are a series of graphs illustratinga utilization in policy (

₁) as a function of commitment factor κ;

FIG. 3 is a graph of a utilization in policy (

₂) as a function of θ;

FIG. 4(a), FIG. 4(b), and FIG. 4(c) are a series of graphs illustratingprobability of over-utilization in policy (

₁), P(U_(CPU)≥0.95), as a function of the coefficient of variation;

FIG. 5 is a graph of a probability of over-utilization in policy (

₂) as coefficient of variation increases;

FIG. 6 is a graph illustrating that over-utilization probability in

₃ is independent from demand variations;

FIG. 7 is a graph illustrating the relationship between the commitmentfactor, K, and the coefficient of variation for different D_(CPU) ^(max)values for

₁;

FIG. 8 is a graph illustrating the relationship between θ and thecoefficient of variation for

₂;

FIG. 9(a) is a graph a in [0,1], which maximizes entropy;

FIG. 9(b) is a graph of the corresponding Beta distribution of FIG.9(b);

FIG. 10 and FIG. 11 are a flow chart of managing requests for anadditional virtual machine; and

FIG. 12 is a diagram illustrating an example cloud computing system ornode;

FIG. 13 is a diagram illustrating an example cloud computing environmentwith FIG. 12; and

FIG. 14 is a diagram of an example abstraction model layers in a cloudcomputing environment of FIG. 13.

DETAILED DESCRIPTION

As required, detailed embodiments are disclosed herein; however, it isto be understood that the disclosed embodiments are merely examples andthat the systems and methods described below can be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present subject matter in virtually anyappropriately detailed structure and function. Further, the terms andphrases used herein are not intended to be limiting, but rather, toprovide an understandable description of the concepts.

The description of the present invention has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

Overview

The behavior of various admission control policies under dynamicresource demand is investigated. Introduced is a method for configuringadmission control policies against over-utilization. A measurement-basedadmission control policy for the Cloud is described by approximating thepdf of the aggregated resource utilization using the first and secondmoments. Then, we employ (1) as the admission criterion to decide if thestatistical properties of an arriving VM request will likely to drivethe physical machine into over-utilization. Thus, we enforce anadmission criterion that guarantees a bound on the probability ofover-utilization. We compare the performance of two parameter-basedadmission control policies against a measurement-based control policythat we introduce. We also show how to configure the parameter-basedadmission control policies by using the probabilistic bound of themeasurement-based policy. Note that parameter-based approaches provide adeterministic bound against worst case scenarios and their admissioncriteria do not change with the variations of resource demand We alsoshow how much parameter-based control policies are sensitive tovariations in dynamic resource demand Novelty includes the use ofstochastic properties of resources for managing resource utilization.

We describe the problem formulation in Section 1.0. Two parameter-basedand one measurement-based admission control policies are introduced inSection 2.0. In Section 3.0 we describe how to configure the admissioncontrol criteria to reduce the likelihood of over-utilization. Oursimulation results are presented in Section 4.0. Section 5.0 discussesthe Choice of Beta Distribution and Section 6.0 the parameters of theBeta distribution. Section 7.0 discusses adding a VM. The Distributionof the number of requests accommodated in the Cloud is discussed inSection 8.0. High level flow charts are discussed in Section 9.0. Othertechniques are reviewed in Section 10 followed by conclusions in Section11.0. Section 12.0 is an explanation of a generalized Cloud computingenvironment in which the claimed invention may be practiced.

Overall System Diagram

Turning now to FIG. 1, shown is an overall system diagram. For every VMrequest 100, the mean and the variance of the demand for resource k isrecorded in 110. This information is used in “Compute the maximum VMs”120 to compute the maximum number of VMs, N_(max), demanding resource kthat can be accommodated in physical machine 160. Maximum number of VMsis the number of VMs that are accommodated without exceeding theprobability of overutilization for resource k beyond a threshold. Thiscan be computed by using a probabilistic model 170 for resourceutilization based on the mean and the variance of utilization ofresource k. The mean and the variance of the utilization for resource kis monitored in 150. For every VM request arrival 100, the mean and thevariance is incremented by the amount of arrival's mean demand andvariance. How to compute the maximum number of VMs accommodated by thesystem is explained in the embodiment. Admission Policy 130 may use thecounter for the number of admitted VMs 140 to determine if the with thenew request N_(max), will be exceeded. If the new arrival causes themaximum number in the system exceeds N_(max), the request is rejected.180 takes the N_(max), value calculated by our method to optimize theparameters for other policies (i.e. overcommitment, undercommitmentetc.).

1.0 Formulation And Analysis 1.1. Homogenous System

Consider p homogenous PMs with K different resources with each havingcapacity C_(k), subjected to a stream of homogenous requests with aPoisson arrival process with rate λ and a generally distributed lifetimewith mean T. A request has a demand D_(k) for resource k that isgenerally distributed with distribution function F_(D) _(k)(d_(k))=Pr[D_(k)≤d_(k)], where d_(k) ϵ[D_(k) ^(min), D_(k) ^(max)].Without loss of generality we assume that D_(k) ^(min)=0 and D_(k)^(max)>0. We denote the mean and standard deviation of the demand forresource k by ρ_(D) _(k) and σ_(D) _(k) , respectively. Hence, the meanoffered load for the k^(th) resource is given by

ρ_(k)=λτμ_(D) _(k) .  (2)

Let Z_(k) ^(n) denote the sum of n independent k^(th) resource demandsGiven that Z_(k) ^(n)=n D_(k), the mean of Z_(k) ^(n) is E[Z_(k) ^(n)]=nμ_(D) _(k) , the variance is V[Z_(k) ^(n)]=n σ_(D) _(k) ², and theprobability distribution, denoted by F_(z) _(k) _(n) (z_(k)), is then-fold convolution of F_(D) _(k) (d_(k)).

1.2. Heterogeneous System

The same notations can be extended to a system that is subjected toheterogeneous requests. A request type is characterized by the amount ofdemand for resources. A request is classified to type i=1, 2, . . . , I,where I is the number of types. Let the system consist of, as before, phomogenous PMs with each having C_(k) capacity for the resource type k.A type i request has a demand of D_(ik) for the resource k with mean andstandard deviation of μ_(D) _(ik) and σ_(D) _(ik) , respectively, and adistribution function of F_(D) _(ik) (d_(ik))=Pr[D_(ik)≤d_(ik)], whered_(ik)ϵ[D_(ik) ^(min),D_(ik) ^(max)]. Similar to the homogenous system,we assume that D_(ik) ^(min)=0 and D_(ik) ^(max)>0. Let λ_(i) representsthe mean arrival rate for Poisson arrivals and τ_(i) represent thegenerally distributed mean lifetime of the i^(th) request type. Hence,the mean offered load for a given type i for the k^(th) resource isgiven by

ρ_(ik)=λ_(i)τ_(i)μ_(D) _(ik) .  (3)

The total mean offered load for resource k is:

ρ_(k)=Σ_(i=1) ^(I)ρ_(ik)=Σ_(i=1) ^(I)λ_(i)τ_(i)μ_(D) _(ik) .  (4)

Let n=(n₁, n₂, . . . , n_(I)) denote the number of requests of each ofthe I types in the system. Given n, the sum of independent k^(th)resource demands in the heterogenous system, Z_(n) ^(k) is given by,Z_(n) ^(k)=Σ_(i=1) ^(I)n_(i) D_(ik) where n_(i) represents the number oftype i requests. The mean of Z_(k) ^(n) is E[Z_(k) ^(n)]=Σ_(i=1)^(I)n_(i)μ_(D) _(ik) , the variance is V[Z_(k) ^(n)]Σ_(i=1) ^(I)n_(i)σ_(D) _(ik) ², and the probability distribution, denoted by F_(z) _(k)_(n) (z_(k)), is the convolution of F_(D) _(ik) (d_(ik)) over i.

2.0 Cloud Admission Policies

An admission controller admits a request into the Cloud based on somepolicy

(ϕ)), with a set of parameters ϕ used in admission criteria. Theparameter set ϕ includes elements that characterize the requests, suchas the maximum demand, average demand and elements that characterize theresources in the cloud such as resource capacity. As discussed in theIntroduction section, if the admission criterion uses fix parametervalues based on the characterization of the input request and the Cloudresource, we call the admission policy parameter based. On the otherhand, if the admission criterion uses measurements to capture thestochastic nature of the current state, such as the mean and thevariance of resource utilization, we call it measurement based.

In this section, for the sake of simplicity, we detail the descriptionof policies for homogenous system only. In section 4, we show how thesepolicies can be extended to a heterogeneous system without loss ofgenerality. In parameter based admission control policies, the maximumnumber of requests that can be accommodated by the Cloud for eachresource k is denoted by N_(k) ^(max). Let n be the number of requestsin the PM at the time admission policy is applied. Thus, a request isadmitted to a PM if n<N_(k) ^(max) and is rejected for that particularPM otherwise. If a request cannot be placed to any of the PMs, it isrejected from the Cloud. By using the resulting request rejectionprobability δ, the mean utilization of k^(th) resource U_(k) iscalculated as

$\begin{matrix}{\overset{\_}{U_{k}} = {\frac{\left( {1 - \delta} \right)\; \rho_{k}}{C_{k}p}.}} & (5)\end{matrix}$

We consider three policies from the class of admission policiesdescribed above. The first two admission control policies are parameterbased and the third one is measurement based.

-   -   1. Admission based on the maximum value of demand with a        commitment factor:        ₁(κ, D_(k) ^(max)), where D_(k) ^(max) is the maximum demand for        resource k, κ is the commitment factor for the resources on a PM        and κ>1.    -   2. Admission based on the average value of the demand with a        commitment factor:        ₂(θ, μ_(D) _(k) ), where μ_(D) _(k) is the average demand of a        request for resource k and θ is the commitment factor for the        resources on a PM and 0<θ<1.    -   3. Admission based on a probabilistic bound over-utilization:        ₃ (U_(k)* , ε, μ_(k), σ_(k)), where U_(k)* is the utilization        threshold and ε_(k) is the probabilistic bound on the        over-utilization probability for resource k such that the        probability of over-utilization being above U_(k)* is limited to        ε_(k). μ_(k) and σ_(k) are the mean and the variance of the        utilization for resource k.

Details of these three different admission policies are explained below.

2.1 Policy 1: Admission Based on the Max. Value of Demand with aCommitment Factor

In this admission control policy, the maximum demand values for theresources are taken into account for admission decision. Let us denotethis policy with

_(i)(κ, D_(k) ^(max)), where K is the commitment factor for all types ofresources and D_(k) ^(max) is the maximum value of the demand forresource type k. The respective maximum allowed concurrent requestsN_(max)(

₁) in a PM for this admission policy is

$\begin{matrix}{{N_{\max}\left( {\mathbb{P}}_{1} \right)} = {\min\limits_{k \in K}{\left\{ \left\lfloor \frac{\kappa \; C_{k}}{D_{k}^{\max}} \right\rfloor \right\}.}}} & (6)\end{matrix}$

Here K is the number of resources a request is demanding in a physicalmachine, PM. This policy accepts requests to a PM as long as the totalnumber of requests in the PM is less than or equal to N_(max)(

₁) at the time of admission. N_(max)(

₁) is the maximum number of requests that can be accommodated withoutover-committing any of the resources beyond κ.

In this policy, the commitment factor, κ is used to prevent theunder-utilization of a resource on a PM. Since the accepted requests arenot always demanding their maximum value, selecting κ>1 reducesunder-utilization. The performance of the Cloud depends on selecting theκ value properly. As long as the requests are demanding less than theirmaximum value, particular PM is guaranteed to function properly withκ=1. This selection, however, will cause under-utilization of theresources. On the other hand, if the VMs that are accepted to that PMdemand their maximum values and when κ>1, then resources on PM face anover-utilization since total demand exceeds the resource capacity. Thisproblem causes “crashing” on the physical machine if particular resourceis a memory. Higher the κ values, the more likely for

₁ to accept VMs thus more likely to over-utilize a resource on a PM.

Note that selecting a value for κ provides a deterministic bound on thenumber of requests to be accepted. In order to utilize the resourceseffectively, κ value needs to be adjusted against the stochasticvariations of resource utilization. In practice, it is not common tochange the κ values frequently.

2.2 Policy 2: Admission Based on the Avg. Value of the Demand withCommitment Factor

In this admission policy, the average demand of a VM request is takeninto account for admission decision. Let us denote this policy with

₂(θ, μ_(D) _(k) ), where θ is the commitment factor for all resourcesand μ_(D) _(k) is the mean value of the demand for the resource type k.The respective maximum allowed concurrent requests N_(max)(

₂) in a PM for this admission policy is

$\begin{matrix}{{N_{\max}\left( {\mathbb{P}}_{2} \right)} = {\min\limits_{k \in K}{\left\{ \left\lfloor \frac{\theta \mspace{11mu} C_{k}}{\mu_{D_{k}}} \right\rfloor \right\}.}}} & (7)\end{matrix}$

In this admission control schema, requests are accepted to a PM as longas the total number of requests in the PM is less than or equal toN_(max)(

₂) at the time of admission.

The commitment factor, θ is used to prevent the over-utilization ofresources on a PM since the accepted requests do not always demand theiraverage value. This factor helps to maintain the utilization ofresources on a PM to be under an upper limit for the times when theadmitted requests demand higher than their average value. Usually, θ isselected as: 0<θ<1. Smaller the θ value, it is more likely to reject therequests thus less likely to over-utilize resources on a PM.

Note that N_(max) (

₂) is a deterministic bound. As in the case of

_(i), the parameter of

₂, θ, needs to be adjusted when the demand at a particular instance isdifferent from the average demand, μ_(D) _(k) in order to maintain theutilization under a certain threshold. Frequent adjustments to θ,however, is not practical as in case of Policy 1.

2.3 Policy 3: Admission Based on a Probabilistic Bound Over Utilization

In this admission policy, dynamic nature of a demand for a resource isrepresented with its mean, μ_(D) _(k) and standard deviation, σ_(D) _(k). Let each PM consists of κ resources, the utilization of each resource,U_(k), is a random variable between [0,1] and characterized by its firstand second moments:

U_(k): k^(th) resource utilization of PM where k≤K

μ_(k): Mean of U_(k)

σ_(k): Standard deviation of U_(k)

We approximate the probability distribution function (pdf) of U_(k) as aBeta distribution since Beta distribution is a good approximation forthe maximum entropy probability distribution for all classes ofdistributions with the same first and second moments (see Section 6entitled “Parameters Of The Beta Distribution” for the reasoning of thisassumption). Beta distribution is a family of continuous probabilitydistributions defined on the interval [0,1] by two positive shapeparameters, denoted by α and β. Hence, we also characterize theutilization U_(k) with two parameters, α and β, associated with thefirst and second moments of U_(k). For more information on Betadistribution, see Section 7 entitled “Adding A VM”.

Admission criterion for Policy 3,

₃(U_(k)*, ε_(k), μ_(k), σ_(k)), utilizes U_(k)*, ε_(k), μ_(k) and σ_(k)to make an admission decision. Here U_(k)* is the over-utilizationthreshold, ε_(k) is the probabilistic bound on over-utilization, μ_(k)and σ_(k) are the estimated mean and the standard deviation of themeasured utilization of resource k after the request arrival. Theadmission criterion for Policy 3 is given by

F _(z) _(k) _(n) (U _(k)*)≥(1−ε_(k))  (8)

If equation (8) is not satisfied with the new request arrival, therequest is rejected. It is important to note that equation (8) can bealso written as F_(z) _(k) _(n) (U_(k)*)<ε_(k), i.e. utilization of thecomputer resource being less than a probabilistic bound.

The respective maximum allowed concurrent requests N_(max)(

₃) in a PM for Policy 3 is expressed as:

$\begin{matrix}{{N_{\max}\left( {\mathbb{P}}_{3} \right)} = {\min\limits_{k\; \in K}{\left\{ {\sup \left\{ {n{{F_{Z_{k}^{n}}\left( U_{k}^{*} \right)} \geq \left( {1 - ɛ_{k}} \right)}} \right\}} \right\}.}}} & (9)\end{matrix}$

Here U_(k) ε[0,1] is the utilization of resource k and U_(k)˜Beta(α_(k),β_(k)). As described in Appendix B, the corresponding α_(k) and β_(k)values are found from the estimated mean and variance values of theutilization of resource k in the PM as:

$\begin{matrix}{\alpha_{k} = {{\overset{\_}{R}}_{k}\left( {\frac{{\overset{\_}{R}}_{k}\left( {1 - {\overset{\_}{R}}_{k}} \right)}{{\overset{\_}{S}}_{k}^{2}} - 1} \right)}} & (10) \\{\beta_{k} = {\left( {1 - {\overset{\_}{R}}_{k}} \right)\left( {\frac{{\overset{\_}{R}}_{k}\left( {1 - {\overset{\_}{R}}_{k}} \right)}{{\overset{\_}{S}}_{k}^{2}} - 1} \right)}} & (11)\end{matrix}$

where R and S are estimations for μ_(k) and σ_(k) respectively. Hencethe cumulative distribution function F_(z) _(k) _(n) (U_(k)) for theutilization of resource k is expressed as:

F _(z) _(k) _(n) (U _(k),α_(k),β_(k))=B(U_(k),α_(k),β_(k))/B(α_(k),β_(k))  (12)

where B is the Beta function. Note that N_(max)(

₃) is not a deterministic bound, but it changes as the mean and thevariance of the utilization change dynamically. Unlike

₁ and

₂,

₃ does not need to be adjusted, since

₃ is dynamically adjusted with the measured statistical properties ofresource utilization. The predefined thresholds for U_(k)* and ε_(k) areset by the Cloud manager depending on the specifications of the physicalmachine.

3.0 Configuration of Admission Policies 3.1 Homogenous System

So far, we have obtained maximum allowed concurrent requests N_(max)(

_(k)) in a physical machine for each policy as:

${1.\mspace{14mu} {N_{\max}\left( {\mathbb{P}}_{1} \right)}} = {\min_{K}\left\{ \left\lfloor \frac{\left. {\kappa \; C_{k}}\; \right\rfloor}{D_{k}^{\max}} \right\rfloor \right\}}$${2.\mspace{14mu} {N_{\max}\left( {\mathbb{P}}_{2} \right)}} = {\min_{K}\left\{ \left\lfloor \frac{\theta \mspace{11mu} C_{k}}{\mu_{D_{k}}} \right\rfloor \right\}}$3.  N_(max)(ℙ₃) = min_(K){sup {nF_(Z_(k)^(n))(U_(k)^(*)) ≥ (1 − ɛ_(k))}}.

The first two are deterministic bounds for the number of concurrentrequests that can be accommodated in the Cloud and the third one is aprobabilistic bound. The first bound guarantees accommodation in theCloud for requests with maximum demand D_(k) ^(max) on resource k with acommitment factor κ. Similarly, the second bound guaranteesaccommodation in the Cloud for requests with the average demand μ_(D)_(k) on resource k with a commitment factor θ. Note that thesedeterministic bounds cannot guarantee that resources will not beover-utilized. Also, there is no guarantee that the requests will be notbe rejected as long as resources are available at the time arrival. Thisis merely because the first and the second admission control policies donot take into account the dynamic nature of the resource utilization ofexisting virtual machines in the Cloud. The third policy, however, usesthe measured utilization statistics in the admission criterion, thusN_(max)(

₃) is continuously adjusted.

For the same request arrival process, all three policies perform thesame when N_(max)(

₁)=N_(max)(

₂)=N_(max)(

₃). In this case, the rejection rate, as well as the overload factor,will be the same for all policies. As the statistical characteristics ofPM resource utilization change, the over-utilization probability inPolicy 3 remains below ε_(k). This is not the case for Policy 1 and 2.Regardless, over-utilization probabilities for the first two policiescan be controlled by configuring κ and θ values using the measuredresource utilization statistics and N_(max)(

₃)

Let the estimated mean and standard deviation of utilization be μ _(k)and σ _(k) for resource k, respectively. The next request with demandstatistics μ and σ will increase the mean utilization to μ _(k)*=μ_(k)+μ and the variance to σ _(k) ²*=σ _(k) ²+σ². As a result,over-utilization probability after the new arrival is found as in (12),

F _(z) _(k) _(n) (U _(k)*,α_(k)*β_(k)*)=Pr(U _(k) >U _(k)*).  (13)

Here α_(k)* and β_(k)* are found from the estimated mean and variance, μ_(k)* and σ _(k) ²*, respectively, as explained in equations (10) and(11). If equation (13) is greater than the probabilistic bound ε_(k),then the request is rejected.

Assume that the resource utilization demand for all independent arrivalrequests have the same mean and variance, μ and σ, the probability ofover-utilization for N concurrent requests is given by

F _(z) _(k) _(n) (U _(k)*,α_(k)(N),β_(k)(N))=Pr(U _(k)(N)>U_(k)*).  (14)

Here U_(k)(N) is the resource utilization of N concurrent requests forthe resource k. The parameters of the beta function α_(k)(N) andβ_(k)(N) are obtained from (10) and (11) by substituting R=Nμ_(k) and S²=Nσ_(k) ². This yields the probabilistic bound N_(max)(

₃) as defined above. N_(max)(

₃) is the smallest number of concurrent requests that causesover-utilization for any resource k. Once N_(max)(

₃) is computed with estimated values of μ_(k) and and σ_(k), then thecommitment factors for Policy 1 and 2 are found as follows:

$\begin{matrix}{{\kappa = \frac{{N_{\max}\left( {\mathbb{P}}_{3} \right)}\; D_{k}^{\max}}{C_{k}}}{\theta = {\frac{{N_{\max}\left( {\mathbb{P}}_{3} \right)}\; \mu_{D_{k}}}{C_{k}}.}}} & (15)\end{matrix}$

Since

₃ considers the dynamic demand, it can be used to tune the parameters ofthe other policies. This forces the system to run with same rejectionrate, thus with the same overload factor for all three policies. As theprobability distribution function of requests, F_(z) _(k) _(n) ,changes, new N_(max)(

₃) values are calculated, thus new parameters for

_(i) and

₂ are generated.

We have also shown how to compute the distribution of the number ofrequested arrivals in the Cloud by using independent arrival assumptionsin Section 6.0 entitled “Distribution Of the Number Of RequestsAccommodated in The Cloud” below. This approximation may be useful toestimate the under-utilization probabilities.

3.2 Heterogeneous System

For the heterogeneous case, admission controller still admits a requestinto a PM based on n, the number of requests in the PM at the time ofadmission policy

(ϕ) is applied. Denote the resulting request rejection probability byδ_(i), and the mean utilization of k^(th) resource by U_(k) . It isgiven by

$\begin{matrix}{\overset{\_}{U_{k}} = {\frac{\sum\limits_{i = 1}^{I}{\left( {1 - \delta_{i}} \right)\; \rho_{ik}}}{C_{k}\; {{PM}}}.}} & (16)\end{matrix}$

The three policies that are described before are still applicable in theheterogeneous system with some modifications. The N_(max) value that iscalculated by policies now have a list of arrays where each arraycontains feasible combination of acceptable numbers of each type of VMs.Let N_(max) ^(j) represent one of the possible combination of feasiblemaximum vector for different types of VMs such that N_(max) ^(j)={n¹, .. . , n^(I)}, where n^(i) represents the number of allowed type irequests in the PM and there is a total of I types of requests. TheN_(max) ={N_(max) ¹, . . . , N_(max) ^(J)} represents all possiblecombinations of feasible admissions that will reveal the same maximumvalue and there is a total of J number of lists that satisfy the totalmaximum number of requests including all types.

Let the policies be now adjusted per request type i such that;

₁(κ, D_(ik) ^(max)),

₂(θ, μ_(D) _(ik) ), and

₃ (U_(k)* , ε, μ_(D) _(ik) , σ_(D) _(ik) ). Each of the above policiesper type determines its respective maximum value on the allowedconcurrent request type i by assuming that the number of other types ofrequests placed in the same physical machine is zero. In particular, wehave an upper bound on n_(i) values such that:

${1.\mspace{14mu} {n^{i}\left( {\mathbb{P}}_{1} \right)}} \leq {\min_{K}\left\{ \left\lfloor \frac{\left. {\kappa \; C_{k}}\; \right\rfloor}{D_{ik}^{\max}} \right\rfloor \right\}}$${2.\mspace{14mu} {n^{i}\left( {\mathbb{P}}_{2} \right)}} \leq {\min_{K}\left\{ \left\lfloor \frac{\theta \mspace{11mu} C_{k}}{\mu_{D_{ik}}} \right\rfloor \right\}}$3.  n^(i)(ℙ₃) ≤ min_(K){sup {nF_(Z_(k)^(n))(U_(k)^(*)) ≥ (1 − ɛ_(k))}}

By using the relationships between upper limits and D_(ik)s, we canwrite the list of N_(max) ={N_(max) ¹, . . . , N_(max) ^(J)} andconstruct admission decision based on these vectors.

4.0 Numerical Results 4.1 Description of Setup

We consider a Cloud with 15 PMs, each with a CPU capacity of 80 cores.There are two types of VM requests, small and large depending on theirsize hence we are working with a heterogeneous system. A small VMrequires 2 CPU cores and a large VM requires 10 CPU cores on average.The variation in demand is characterized by the coefficient of variationwith respect to demand and is in the range of 0.5 to 5 with 0.5increments. The over-utilization threshold for CPU is set to 95% and itis not allowed for the utilization to violate this threshold more than1% of time.

We simulate the above system, starting from an empty system, leading upto an offered loading of 95% average PM CPU utilization. VM requestsarrive as a Poisson process and the lifetime of a VM is exponentiallydistributed in such a way to maintain the 95% average load. The mix ofsmall and large VMs is governed by a Bernoulli process withprobabilities 0.3 and 0.7, respectively.

We implemented the three policies under study. First, we investigatedthe impact of the parameters of the policy on its performance. Then, wecompared the behavior of the policies with respect to demand variation.Later, we showed how to configure parameter based policies by usingPolicy 3.

4.2 Effect of Parameters on Mean Utilization

In order to compare the performances of parameter-based policies

₁ and

₂ in terms of utilization we vary tunable parameters and observe thebehavior of mean utilization. For

₁(κ, D_(k) ^(max)), we varied κ from 1 to 10 with increments of 0.5 forthree different D_(max) values: {4,20}, {8,40}, {20,100}, where eachtuple represents the value of D_(max) for small and large VMs,respectively. We obtained the rejection rates from the simulator andcalculated the mean utilization using (16). As depicted in FIG. 2(a),(b), (c) we observe an increase in mean utilization with the increase inthe commitment factor, for all maximum demand values. Further, weobserve that for the same commitment factor κ, as the D_(CPU) ^(max)decreases, utilization increases since smaller D_(CPU) ^(max) valuesadmit more thus the policy rejects less number of requests. FIG. 2 is autilization in policy (

₁) as a function of commitment factor κ. More specifically, FIG. 2(a)illustrates D_(CPU) ^(max)={4,20}, FIG. 2(b) is D_(CPU) ^(max)={8,40},and FIG. 2(c) D_(CPU) ^(max)={20,100}.

For

₂ (θ, μ_(D) _(k) ), we varied θ in the range of [0.5,1] with incrementsof 0.05 for the mean demand of {2,10}. The mean utilization as afunction of θ is depicted in FIG. 2. We observe that an increment in θvalue allows the admission controller to accept more requests, thusincreasing the mean utilization. For a utilization in policy (

₂) as a function of θ refer to FIG. 3.

FIG. 2 and FIG. 3 show the corresponding resource utilization forselected policy parameters and provide a benchmark for Cloudadministrators to select parameter values for the desired utilizationvalues. As an example, when D_(CPU) ^(max) is {4,20} for different VMtypes, the average utilization is limited to 0.90 if κ=8 is chosen asseen in FIG. 2 for the 95% average load in Policy 1. Similarly, thecorresponding θ value for the desired average utilization for Policy 2is found from FIG. 3.

₃, on the other hand, is not parameter-based and it ensures the givenquality-of-service and keeps the utilization below the desired levelbased on the measured resource utilization statistics.

4.3 Effect of Demand Variation on Probability of Over-Utilization

In

₁ and

₂, resource demand variations are not taken into account in selectingthe policy parameters. In this section, we investigate the impact ofvariation in CPU demand on probability of over-utilization with respectto fixed parameters of

₁ and

₂.

For

₁, we fixed the commitment factor, K, to {2,5,8,10} for various levelsof fixed D_(CPU) ^(max): {4,20}, {8,40}, {20,100} values. We obtainedthe average number of accepted requests from the simulator andcalculated the convoluted probability distribution function as describedin section 2. FIG. 4(a), (b), (c) depicts that for all κ and D_(CPU)^(max) values, as the coefficient of variation increases, theprobability of over-utilization increases due to increase in theuncertainty. (FIG. 4 is a graph illustrating probability ofover-utilization in policy (

₁), P(U_(CPU)≥0.95), as a function of the coefficient of variation inlogarithmic scale.) For the same κ values, higher D_(CPU) ^(max) valuesresult in rejecting more requests thus yielding less over-utilizationprobability. For instance, the probability of over-utilization for thesame coefficient of variation is less in FIG. 4(c) than FIG. 4(a).Moreover, when the coefficient of variation increases for fixed D_(CPU)^(max) values (any D_(CPU) ^(max)), κ values need to be decreased toreduce the chance of over-utilization. This experiment clearly indicatesthat the impact of demand variations can be fenced by selectingappropriate κ values in

₁ and likelihood of higher-utilization is reduced.

Similarly, in

₂, as the coefficient of demand variation increases, the probability ofover-utilization increases regardless of the commitment factor, θ. FIG.5 also shows that for higher θ values, the probability ofover-utilization is higher. (FIG. 5 is a graph of the probability ofover-utilization in policy (

₂) as coefficient of variation increases in logarithmic) When thecoefficient of variation increases, θ values need to be decreased inorder to reduce the probability of over-utilization. Thus, in order tolimit the probability of over-utilization below a threshold, E for

₂, the commitment factor, θ, needs to be tuned accordingly.

Since

₃ is a measurement-based policy, it always ensures that the probabilityof over-utilization is under ε, which is 0.01 for this specific example,regardless of changes in coefficient of variation. FIG. 6 illustratesthat over-utilization probability in

₃ does not depend on demand variations. Stated differently, FIG. 6 is agraph illustrating a probability of over-utilization in policy (

₃) as coefficient of variation increases.

Unlike

₁ and

₂,

₃ monitors the system and makes the acceptance decision based on thestate of the Cloud in terms of over-utilization probability. Not only itensures the stability of the Cloud but also it can be used to tune theparameters of

₁ and

₂ to keep the over-utilization probability under ε threshold. Therelationship between

₃ and other policies is described in the next section.

4.4 Relationship Between Policies

As previous experiments indicate, admissions with

₁ and

₂ with fixed parameters will under-perform as the coefficient ofvariation of demand changes, whereas

₃ always ensures the required quality-of-service for the Cloud. Thecommitment factor, κ, for

₁ and θ for

₂ need to be adjusted as the variation in the CPU demand changes inorder to maintain the quality-of-service on over-utilization. One canuse the probability of over-utilization function for

₃ to adjust the parameters of the other two policies. We obtained theaverage number of accepted requests by

₃ from the simulator and used (??) to obtain various κ values fordifferent levels of variations. FIG. 7 shows the relationship betweenthe commitment factor, κ, and the coefficient of variation for differentD_(CPU) ^(max) values for

₁. Stated differently, FIG. 7 is a graph illustrating maintaining (

₃) utilization in (

₁) by tuning commitment factor. We note that the probability ofover-utilization is kept under ε in

₁ by employing the same number of concurrent requests allowed N_(max)(

₃) that we found for

₃ in

₁. For instance, by employing this approach, as the coefficient ofvariation increases from 0.5 to 2.5 for D_(CPU) ^(max)={4,20}, κ valueneeds to be dropped from 6 to 2 in order to keep the probability ofover-utilization under 0.01. The relationship between κ and thecoefficient of variation follows an exponential behavior, and as D_(CPU)^(max) values increases, the degree of the exponent decreases.

Similarly, admissions under

₂ is not responsive to changes in coefficient of variation unless thecommitment factor, θ, is adjusted. FIG. 8 shows the relationship betweenθ and the coefficient of variation for

₂. FIG. 8 illustrates maintaining (

₃) utilization in (

₂) by tuning commitment factor. We note that

₂ maintains the probability of over-utilization under ε by again usingthe probability of over-utilization function of

₃. There is almost a linear relationship between these two attributesfor μ={2,10}. For instance, when the coefficient of variation increasesfrom 2 to 4, θ value needs to be dropped from 0.6 to 0.25.

5.0 Choice of Beta Distribution

We select a probability distribution for the utilization of resource k,U_(k), from the first and second moments of the distribution. Ingeneral, when given the first few moments of a probability distribution,the most likely distribution function is the one that maximizes entropy.As an example, given a mean of 0.6 and a standard deviation of 0.2 for adistribution in [0,1], we plot the distribution which maximizes entropyin FIG. 9(a). Matching the first and second moments, we plot thecorresponding Beta distribution in FIG. 9(b). As noted, the Betadistribution approximates well the maximum entropy distribution.

6.0 Parameters of the Beta Distribution

Beta distribution is a family of continuous probability distributionsdefined on the interval [0,1] by two positive parameters, denoted by αand β with following probability density function:

$\begin{matrix}{{f\left( {{x;\alpha},\beta} \right)} = \frac{{x^{\alpha - 1}\left( {1 - x} \right)}^{\beta - 1}}{B\left( {\alpha,\beta} \right)}} & (17)\end{matrix}$

where B is the beta function. Hence, we also characterize theutilization x with two parameters, α and β, associated with the firstand second moments of x. First moment of the beta distribution: Usingthe method of moments estimator, the sample mean R and the variance S ²of the observed utilization are set to the population mean and thevariance and expressed in terms of the mean and the standard deviationof the associated beta distribution as:

$\begin{matrix}{\overset{\_}{R} = \frac{\alpha}{\alpha + \beta}} & (18) \\{{\overset{\_}{S}}^{2} = \frac{\alpha\beta}{\left( {\alpha + \beta + 1} \right)\left( {\alpha + \beta} \right)^{2}}} & (19)\end{matrix}$

From (18) and (19), α and β are solved in terms of population mean andvariance as:

$\begin{matrix}{\alpha = {\overset{\_}{R}\left( {\frac{\overset{\_}{R}\left( {1 - \overset{\_}{R}} \right)}{{\overset{\_}{S}}^{2}} - 1} \right)}} & (20) \\{\beta = {\left( {1 - \overset{\_}{R}} \right)\left( {\frac{\overset{\_}{R}\left( {1 - \overset{\_}{R}} \right)}{{\overset{\_}{S}}^{2}} - 1} \right)}} & (21)\end{matrix}$

For the estimated α and β values, the cumulative distribution functioncan be expressed in terms of incomplete beta function F (x; α, β) as:

$\begin{matrix}{{F\left( {{x;\alpha},\beta} \right)} = {\frac{B\left( {{x;\alpha},\beta} \right)}{B\left( {\alpha,\beta} \right)} = {I_{x}\left( {\alpha,\beta} \right)}}} & (22)\end{matrix}$

Here, (22) gives the x^(th) percentile of the beta distribution. As anexample, the 90^(th) percentile of the resource utilization is expressedas F(0.9, α, β)=I_(0.9) (α, β) which is the probability that theutilization is below 90%.

7.0 Adding A VM

The impact of a virtual machine to a physical machine can be measured byhow much the likelihood of exceeding the over-utilization threshold isincreased. If μ and σ are the first and second moments of theutilization demand, respectively, on resource R_(k) by the new arrival,then the new mean and variance value for resource utilization after thearrival is found as:

R′=R+μ  (23)

S ² ′=S ²+σ²  (24)

Here, we assume that the utilization demand of the newly arriving VM isindependent of the utilization of the physical machine. The probabilitydensity function for the new utilization is characterized by α′ and β′values associated with the new mean and variance values. If thecontribution of a virtual machine to the first and second moments of aresource changes the α and β values to α′ and β then the increase in the90^(th) percentile due to this particular virtual machine is found as:I_(0.9) (α, β)−I_(0.9) (α′, β′). As an example if the newly presentedvirtual machine reduces the 90% percentile of the utilization fromI_(0.9) (α, β)=0.25 to I_(0.9) (α′, β;)=0.20, then it is 5% more likelythat the resource utilization will go over 90% utilization with the newarrival. This is the impact of a virtual machine on a physical machine.

8.0 Distribution of the Number of Requests Accommodated in the Cloud

Given an N_(max) we can evaluate the stationary distribution of thenumber of requests in the system by noting that the equivalent model isthat of an M/G/m/m loss queuing system, where m=|PM|×N_(max). Inparticular, in the case of exponentially distributed lifetimes, we havean M/M/m/m loss queue whose stationary distribution is given by

$\begin{matrix}{{\pi_{n} = {\pi_{0}\frac{\left( {\lambda \; \tau} \right)^{n}}{n!}}},{n = 1},2,\ldots \;,m,} & (25)\end{matrix}$

and π₀ is given by the normalizing constant

$\begin{matrix}{\pi_{0} = \left\lbrack {\sum_{n = 0}^{m}\frac{\left( {\lambda \; \tau} \right)^{n}}{n!}} \right\rbrack^{- 1}} & (26)\end{matrix}$

The average occupancy is given by

N=Σ _(n=0) ^(m) =nπ _(n).  (27)

Thus, given N_(max), we have the average utilization of resource k as:

$\begin{matrix}{\overset{\_}{U_{k}} = {\frac{\overset{\_}{N}}{c_{k}}\frac{\mu_{D_{k}}}{{PM}}}} & (28)\end{matrix}$

and the rejection probability

δ=π_(N) _(max) .  (29)

Define an overload factor η(U_(k)*)=Pr[U_(k)>U*]. Since U_(k)=N D_(k),then by conditioning on N we get

$\begin{matrix}\begin{matrix}{{\eta_{k}\left( U_{k}^{*} \right)} = {\sum_{n = 1}^{N_{\max}}{\pi_{n}{\Pr \left\lbrack {U_{k} > U_{k}^{*}} \middle| n \right\rbrack}}}} \\{= {\sum_{n = 1}^{N_{\max}}{{\pi_{n}\left( {1 - {F_{z_{k}^{n}}\left( U_{k}^{*} \middle| n \right)}} \right)}.}}}\end{matrix} & (30)\end{matrix}$

8.0 System D 9.0 Example Flow Charts

FIG. 10 and FIG. 11 are an example flow chart of managing requests foran additional virtual machine. The process begins in step 1002 andimmediately proceeds to step 1004 where a cluster or cloud systemincludes at least one virtual machine accessing at least one computerresource associated with at least one physical machine. In step 1006,one or more non-deterministic virtual machine request for the computerresource are received. An over-utilization of the computer resource as aprobability distribution function is modeled in step 1008. For exampleequation 8 above re-written as F_(z) _(k) _(n) (U_(k)*)<ε_(k). Based ona probability of a utilization of the computer resource being greaterthan a probalistic bound on the over-utilization of the computerresource, allowing admission of an additional virtual machine on thephysical machine associated with the computer resource, steps 1010 and1014. Otherwise, the request for the computer resource is rejected instep 1012 and the process continues bask to step 1006 or the flowterminates (not shown). In step 1014, the admission of an additionalvirtual machine on the physical machine associated with the computerresource is allowed.

An optional step 1016, shown in broken lines may be used to calculateN_(max*) wherein the maximum number of allowed concurrent computerresource requests in a policy is given by

${N_{\max} = {\min\limits_{k \in K}\left\{ {\sup \left\{ n \middle| {{F_{z_{k}^{n}}\left( U_{k}^{*} \right)} \geq \left( {1 - ɛ_{k}} \right)} \right\}} \right\}}},$

where sup is a supremum subset and n is a sum of independent k^(th)computer resource request. The supremum (sup) of a subset S of a totallyor partially ordered set T is the least element of T that is greaterthan or equal to all elements of S. In our description it is the n^(th)virtual machine that violates the acceptance condition for Policy 3above. That is a condition is the probability of overutilization isabove the epsilon for the first time. For example, assume k=1 and let'ssay we accepted 5 VMs so far without violating the condition and ourcondition is still valid. When the 6th VM comes, the condition isviolated. The N_(max) is 6 for this specific example. Any number of Vm'sgreater than 6 is going to violate the over utilization probabilitycondition for that one resource.

The maximum allowed VMs on a PM for one resource is given by Section 3.0above {sup{n|F_(z) _(k) _(n) (U_(k)*)≥(1−ε_(k))}}.

This may be used to limit the addition of new virtual machines or inconjunction with other policies e.g. maximum number of allowedconcurrent computer resource requests in a policy for the physicalmachine is given by a minimum of a supreme subset of independentcomputational resource requests and the probability of the utilizationof the computer resource.

Since we also capture more than one resource type, we calculate (1) foreach resource type (we have k resource types) and pick the minimum ofall since the minimum of them will be the bottleneck resource thatdetermines the N_(max)(

₃)=min_(K){sup{n|F_(z) _(k) _(n) (U_(k)*)≥(1−ε_(k))}}.

The dynamic aspect of updating the probability distribution function isshown in step 1018. For example in response to at least one of 1) anadditional non-deterministic virtual machine request; 2) a change in theutilization of the computer resource by the virtual machine; 3) a changein the utilization of the computer resource by any additional virtualmachine which has been added, the process continues to loop back to step1006.

In another optional step, N_(max) can then be used with either anover-commitment placement polity or a under commitment placement policyor both in step 1020. The process end in step 1022.

10.0 Other Techniques

The problem of admission control in data centers, and in the Cloud ingeneral, has been addressed from different angles. In a data center thatis subjected to a stream of VM requests of different types isconsidered. Due to the large size of the problem, an approximate dynamicprogramming technique is proposed. The allocation of VMs on PMs isperformed assuming a fixed demand of resources. But, in practice, thedemand varies over time, suggesting the inclusion of the variability indemand when admitting a VM. This variability in CPU usage enabled theoverbooking of resources.

In the area of task allocation in distributed systems, the variabilityin resource demand has also been addressed. In a genetic algorithm isemployed to dynamically schedule heterogeneous tasks on heterogeneousprocessors. A different approach is proposed in that is based onannealing models and simulated annealing.

Fundamentally, admission control is similar in many ways to losssystems. In such systems, a collection of resources with some capacitiesare provided to a stream of requests, where each request specifies itsresource demand Typically, the resource demand is fixed. Even in thiscase, and given Poisson arrivals of requests and generally distributedrequest residence times, the probabilistic analysis, say to evaluate theloss probability, which corresponds to the rejection rate in admissioncontrol, is challenging due to the need to compute a normalizingconstant. To overcome this challenge, asymptotic approximations havebeen obtained for load factors that correspond to light, critical, andoverload conditions.

Back to the Cloud computing environment, there the variability in demanddepends on the nature of the resource. For example, for the CPU resourceon a PM, the total CPU demand of all VMs hosted on that CPU may verywell exceed 100%. This will simply result in congestion and degradedperformance. However, for the memory resource, such an overload isunacceptable since it may lead to crashing. Hence, a method forestimating the probability of overload is crucial in deciding onadmitting a VM into the system. And, that is exactly one of thenovelties of the present invention.

12.0 Generalized Cloud Computing Environment

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

-   -   Software as a Service (SaaS): the capability provided to the        consumer is to use the provider's applications running on a        cloud infrastructure. The applications are accessible from        various client devices through a thin client interface such as a        web browser (e.g., web-based email). The consumer does not        manage or control the underlying cloud infrastructure including        network, servers, operating systems, storage, or even individual        application capabilities, with the possible exception of limited        user-specific application configuration settings.    -   Platform as a Service (PaaS): the capability provided to the        consumer is to deploy onto the cloud infrastructure        consumer-created or acquired applications created using        programming languages and tools supported by the provider. The        consumer does not manage or control the underlying cloud        infrastructure including networks, servers, operating systems,        or storage, but has control over the deployed applications and        possibly application hosting environment configurations.    -   Infrastructure as a Service (IaaS): the capability provided to        the consumer is to provision processing, storage, networks, and        other fundamental computing resources where the consumer is able        to deploy and run arbitrary software, which can include        operating systems and applications. The consumer does not manage        or control the underlying cloud infrastructure but has control        over operating systems, storage, deployed applications, and        possibly limited control of select networking components (e.g.,        host firewalls).

Deployment Models are as follows:

-   -   Private cloud: the cloud infrastructure is operated solely for        an organization. It may be managed by the organization or a        third party and may exist on-premises or off-premises.    -   Community cloud: the cloud infrastructure is shared by several        organizations and supports a specific community that has shared        concerns (e.g., mission, security requirements, policy, and        compliance considerations). It may be managed by the        organizations or a third party and may exist on-premises or        off-premises.    -   Public cloud: the cloud infrastructure is made available to the        general public or a large industry group and is owned by an        organization selling cloud services.    -   Hybrid cloud: the cloud infrastructure is a composition of two        or more clouds (private, community, or public) that remain        unique entities but are bound together by standardized or        proprietary technology that enables data and application        portability (e.g., cloud bursting for loadbalancing between        clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 12, a schematic of an example of a cloud computingnode is shown. Cloud computing node 1210 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 1210 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 1210 there is a computer system/server 1212,which is operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with computer system/server 1212 include, butare not limited to, personal computer systems, server computer systems,thin clients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 1212 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 1212 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 12, computer system/server 1212 in cloud computing node1210 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 1212 may include, but are notlimited to, one or more processors or processing units 1216, a systemmemory 1228, and a bus 1218 that couples various system componentsincluding system memory 1228 to processor 1216.

Bus 1218 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 1212 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 1212, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 1228 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 1230 and/orcache memory 1232. Computer system/server 1212 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 1234 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 1218 by one or more datamedia interfaces. As will be further depicted and described below,memory 1228 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 1240, having a set (at least one) of program modules1242, may be stored in memory 1228 by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystem, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules 1242 generally carry outthe functions and/or methodologies of embodiments of the invention asdescribed herein. Computer system/server 1212 may also communicate withone or more external devices 14 such as a keyboard, a pointing device, adisplay 1224, etc.; one or more devices that enable a user to interactwith computer system/server 1212; and/or any devices (e.g., networkcard, modem, etc.) that enable computer system/server 1212 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 22. Still yet, computersystem/server 1212 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 1220. Asdepicted, network adapter 1220 communicates with the other components ofcomputer system/server 1212 via bus 1218. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 1212. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

Referring now to FIG. 13, illustrative cloud computing environment 1350is depicted. As shown, cloud computing environment 1350 comprises one ormore cloud computing nodes 1310 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1354A, desktop computer 1354B, laptopcomputer 1354C, and/or automobile computer system 1354N may communicate.Nodes 1310 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1350to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1354A-N shown in FIG. 13 are intended to be illustrative only and thatcomputing nodes 10 and cloud computing environment 1350 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 14, a set of functional abstraction layersprovided by cloud computing environment 1350 (FIG. 13) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 14 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 1460 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 1462 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 1364 may provide the functionsdescribed below. Resource provisioning provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricingprovide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 1466 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and the software or algorithm for managing the utilizationof the resources in the cloud in accordance with the feature invention.

13.0 Non-Limiting Examples

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been discussed above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according to variousembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The description of the present application has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A system for managing requests for an additionalvirtual machine, the system comprising: a memory; a processorcommunicatively coupled to the memory, where the processor is configuredto perform operating at least one virtual machine accessing at least onecomputer resource associated with at least one physical machine within acomputing cluster; receiving at least one non-deterministic virtualmachine request for the computer resource; modeling an over-utilizationof the computer resource as a probability distribution function, whereinthe probability distribution function is a Beta distribution function torepresent one of a plurality of probability distribution functions;based on a probability of a utilization of the computer resource beingless than a probalistic bound on the over-utilization of the computerresource, allowing admission of an additional virtual machine on thephysical machine associated with the computer resource, otherwise,rejecting the request for the computer resource.
 2. The system of claim1, wherein the probability of the utilization of the computer resourceis less than an over-utilization threshold U_(k)* is given byF _(z) _(k) _(n) (U _(k)*)≤ε_(k) where the computer resource kassociated with the physical machine, the probabilistic bound is ε_(k),and F_(z) _(k) _(n) is a function of a sum of n independent k^(th)resource demands.
 3. The system of claim 1, wherein at least one of theprobalistic bound ε_(k), and the over-utilization threshold is U_(k)* isset based on an operating specification of the physical machine.
 4. Thesystem of claim 1, further comprising: determining a maximum number ofallowed concurrent computer resource requests in a policy for thephysical machine is given by a minimum of a supreme subset ofindependent computational resource requests and the probability of theutilization of the computer resource.
 5. The system of claim 4, whereinthe maximum number of allowed concurrent computer resource requestsN_(max) in a policy is given by${N_{\max} = {\min\limits_{k \in K}\left\{ {\sup \left\{ n \middle| {{F_{z_{k}^{n}}\left( U_{k}^{*} \right)} \geq \left( {1 - ɛ_{k}} \right)} \right\}} \right\}}},$where sup is a supremum subset and n is a sum of independent k^(th)computer resource request.
 6. The system of claim 5, further comprising:an over commitment placement policy with a maximum value of demand isgiven by$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\left. {\kappa \; C_{k}} \right\rfloor}{D_{k}^{\max}} \right\rfloor \right\}}$and a value of κ is solved by using N_(max), where, D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.
 7. The system of claim 5, further comprising: anunder commitment policy with an average value of demand is given by$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\theta \mspace{11mu} C_{k}}{\mu_{D_{k}}} \right\rfloor \right\}}$and a value of θ is solved by using N_(max), where D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.
 8. The system of claim 1, wherein the probabilitydistribution function is automatically updated in response to at leastone of; an additional non-deterministic virtual machine request; achange in the utilization of the computer resource by the virtualmachine; a change in the utilization of the computer resource by anyadditional virtual machine which has been added.
 9. The system of claim8, wherein based on the probability distribution function being updated,reevaluating the admission of the additional virtual machine on thephysical machine associated with the computer.
 10. A non-transitorycomputer program product for managing requests for an additional virtualmachine, the computer program product comprising a computer readablestorage medium having computer readable program code embodied therewith,the computer readable program code configured to: operating at least onevirtual machine accessing at least one computer resource associated withat least one physical machine within a computing cluster; receiving atleast one non-deterministic virtual machine request for the computerresource; modeling an over-utilization of the computer resource as aprobability distribution function, wherein the probability distributionfunction is a Beta distribution function to represent one of a pluralityof probability distribution functions; based on a probability of autilization of the computer resource being greater than a probalisticbound on the over-utilization of the computer resource, allowingadmission of an additional virtual machine on the physical machineassociated with the computer resource, otherwise, rejecting the requestfor the computer resource.
 11. The non-transitory computer programproduct of claim 10, wherein the probability of the utilization of thecomputer resource is less than an over-utilization threshold U_(k)* isgiven byF _(z) _(k) _(n) (U _(k)*)≥ε_(k) where the computer resource kassociated with the physical machine, the probabilistic bound is ε_(k),and F_(z) _(k) _(n) is a function of a sum of n independent k^(th)resource demands.
 12. The non-transitory computer program product ofclaim 10, wherein at least one of the probalistic bound ε_(k), and theover-utilization threshold is U_(k)* is set based on an operatingspecification of the physical machine.
 13. The non-transitory computerprogram product of claim 10, further comprising: determining a maximumnumber of allowed concurrent computer resource requests in a policy forthe physical machine is given by a minimum of a supreme subset ofindependent computational resource requests and the probability of theutilization of the computer resource.
 14. The non-transitory computerprogram product of claim 13, wherein the maximum number of allowedconcurrent computer resource requests N_(max) in a policy is given by${N_{\max} = {\min\limits_{k \in K}\left\{ {\sup \left\{ n \middle| {{F_{z_{k}^{n}}\left( U_{k}^{*} \right)} \geq \left( {1 - ɛ_{k}} \right)} \right\}} \right\}}},$where sup is a supremum subset and n is a sum of independent k^(th)computer resource request.
 15. The non-transitory computer programproduct of claim 14, further comprising: an over commitment placementpolicy with a maximum value of demand is given by$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\left. {\kappa \; C_{k}} \right\rfloor}{D_{k}^{\max}} \right\rfloor \right\}}$and a value of κ is solved by using N_(max), where, D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.
 16. The non-transitory computer program product ofclaim 15, further comprising: an under commitment policy with an averagevalue of demand is given by$N_{\max} = {\min_{K}\left\{ \left\lfloor \frac{\theta \mspace{11mu} C_{k}}{\mu_{D_{k}}} \right\rfloor \right\}}$and a value of θ is solved by using N_(max), where D_(k) ^(max) is amaximum demand on computer resource k, and, C_(k) is a capacity for thecomputer resource k.
 17. The non-transitory computer program product ofclaim 10, wherein the probability distribution function is automaticallyupdated in response to at least one of; an additional non-deterministicvirtual machine request; a change in the utilization of the computerresource by the virtual machine; a change in the utilization of thecomputer resource by any additional virtual machine which has beenadded.
 18. The non-transitory computer program product of claim 17,wherein based on the probability distribution function being updated,reevaluating the admission of the additional virtual machine on thephysical machine associated with the computer.
 19. Acomputer-implemented method for managing requests for an additionalvirtual machine, the method comprising: operating at least one virtualmachine accessing at least one computer resource associated with atleast one physical machine within a computing cluster; receiving atleast one non-deterministic virtual machine request for the computerresource; modeling an over-utilization of the computer resource as aprobability distribution function, wherein the probability distributionfunction is a Beta distribution function to represent one of a pluralityof probability distribution functions; based on a probability of autilization of the computer resource being greater than a probalisticbound on the over-utilization of the computer resource, allowingadmission of an additional virtual machine on the physical machineassociated with the computer resource, otherwise, rejecting the requestfor the computer resource.
 20. The computer-implemented method of claim19, wherein the probability of the utilization of the computer resourceis less than an over-utilization threshold U_(k)* is given byF _(z) _(k) _(n) (U _(k)*)≥ε_(k) where the computer resource kassociated with the physical machine, the probabilistic bound is ε_(k),and F_(z) _(k) _(n) is a function of a sum of n independent k^(th)resource demands.